新框架通过处理缺失数据增强多模态情感识别能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一个名为Affect-Prototype-Conditioned Fusion (APCF) 的新框架，以改进开放词汇多模态情感识别，特别是在某些数据模态缺失的情况下。这个无候选词生成框架使用情感原型库，根据多样化的情感语义动态约束模态融合。在OV-MERD+和MER-FG数据集上的实验表明，APCF的性能显著优于现有方法。 AI

影响提高了AI从不完整多模态数据中理解情感的能力，可能增强人机交互。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yichi Zhang, Shenyue Wang, Jing Luo, Chunyang Yu, Xinyu Yang · 2026-09-16 04:00

面向开放词汇不完整多模态情感识别的 Affect-Prototype 引导融合

arXiv:2609.16962v1 Announce Type: new Abstract: Open-vocabulary multimodal emotion recognition (OV-MER) aims to generate open natural-language emotion labels from multimodal affective cues. In real-world scenarios, however, complete and synchronized modal data are difficult to ob…

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报道来源 [1]

面向开放词汇不完整多模态情感识别的 Affect-Prototype 引导融合

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